{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt(\"SimpleSurface.dat\", dtype=str)\n",
    "Header = (data[0,:]).tolist()\n",
    "solTchem = (data[1:,:]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['iter', 't', 'dt', 'X(1)', 'HX(6)', 'OX(7)', 'CH3X(8)', 'HOX(9)', 'H2OX(10)', 'CO2X(13)', 'OCX(15)', 'CX(16)', 'CH2X(17)', 'CHX(18)', 'CH4X(19)', 'NOX(20)', 'NO2X(21)', 'HO2X(28)', 'CHOX(45)', 'CHO2X(46)', 'COX(47)', 'H2X(48)', 'NO2X(59)']\n"
     ]
    }
   ],
   "source": [
    "print(Header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makePlotV2(x, y1, y2, y3, info, fig_name):\n",
    "    loc_x = info['loc_x']\n",
    "    loc_y = info['loc_y']\n",
    "    \n",
    "    ys1 = info['label1']['scale']\n",
    "    ys2 = info['label2']['scale']\n",
    "    ys3 = info['label3']['scale']\n",
    "    \n",
    "    label1 = info['label1']['label'] + ' x'+str(ys1)\n",
    "    label2 = info['label2']['label'] + ' x'+str(ys2)\n",
    "    label3 = info['label3']['label'] + ' x'+str(ys3)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=[5,4])\n",
    "    p1 = ax.plot(x, y1/ys1,'r-',label = label1)\n",
    "    p2 = ax.plot(x, y2/ys2,'g-',label = label2)\n",
    "    p3 = ax.plot(x, y3/ys3,'b-',label = label3)\n",
    "    plt.xlabel(info['xlabel'])\n",
    "    plt.ylabel(info['ylabel'])\n",
    "    plt.xscale('log')\n",
    "    \n",
    "    plt.xlim(info['xlim'])\n",
    "    plt.ylim(info['ylim'])\n",
    "     # added these three lines\n",
    "    lns = p1+p2+p3\n",
    "    labs = [l.get_label() for l in lns]\n",
    "    ax.legend(lns, labs, bbox_to_anchor=(loc_x, loc_y),frameon=False)\n",
    "    \n",
    "    plt.savefig(fig_name,bbox_inches='tight')\n",
    "    \n",
    "    return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/np/m42416h166j90krvxmfl40940000gp/T/ipykernel_87123/3915406409.py:21: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  plt.xlim(info['xlim'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "info={}\n",
    "info['label1'] = {'label':'OX','scale':1}\n",
    "info['label2'] = {'label':'X  ','scale':1}\n",
    "info['label3'] = {'label':'CH4X ','scale':5e-2}\n",
    "info['xlabel'] = 'time [s] '\n",
    "info['ylabel'] = 'Site Fraction'\n",
    "info['loc_x'] = 0.5\n",
    "info['loc_y'] = 0.6\n",
    "info['xlim'] = [0,1e-3]\n",
    "info['ylim'] = [0.,1+1e-2]\n",
    "x  = solTchem[:,Header.index('t')]\n",
    "y1 = solTchem[:,Header.index('OX(7)')]\n",
    "y2 = solTchem[:,Header.index('X(1)')]\n",
    "y3 = solTchem[:,Header.index('CH4X(19)')]\n",
    "\n",
    "makePlotV2(x, y1, y2, y3, info, 'SimpleSurface.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
